NLP Fundamentals

NLP enables computers to understand, interpret, and generate human language. Core tasks:

Tokenization — splitting text into words/subwords/characters
POS Tagging — labeling words as noun, verb, adjective, etc.
Named Entity Recognition (NER) — identifying people, places, organizations
Sentiment Analysis — positive/negative/neutral classification
Machine Translation — translating between languages
Text Summarization — generating concise summaries
Question Answering — reading comprehension and factual Q&A

Word Embeddings

Representing words as dense vectors in high-dimensional space so similar words are close together. Word2Vec (Google, 2013) and GloVe were groundbreaking. Transformers now produce contextual embeddings.

Large Language Models (LLMs)

LLMs are transformer models trained on massive text corpora to predict the next token. They exhibit emergent capabilities at scale.

Model	Organization	Parameters
GPT-4o	OpenAI	~1.8T (estimated)
Claude 3.5 Sonnet	Anthropic	~70B (estimated)
Gemini Ultra	Google DeepMind	Unknown
Llama 3.1 405B	Meta (Open Source)	405B

Prompt Engineering

The art of designing prompts to get optimal outputs from LLMs:

Zero-shot: no examples — just ask
Few-shot: provide 2-5 examples before the question
Chain-of-Thought: "Think step by step..." — improves reasoning
System Prompts: define persona, tone, constraints
RAG (Retrieval-Augmented Generation): ground LLM with your own documents

🔮 LLM Hallucinations: LLMs confidently generate plausible-sounding but false information. Always verify factual claims from LLMs against authoritative sources — a critical skill for any AI practitioner.